util.md
In cardinalR: Collection of Data Structures

gen_noise_dims() works

Code
  gen_noise_dims(n = 50, num_noise = 3, min_n = -0.01, max_n = 0.01)
Output
                 [,1]          [,2]          [,3]
   [1,] -0.0081241781  0.0029029013 -9.882231e-03
   [2,] -0.0013916432  0.0085220913 -1.908421e-03
   [3,]  0.0047922982  0.0008789316 -1.908479e-03
   [4,]  0.0028673615 -0.0062801598 -1.132963e-03
   [5,]  0.0080366237  0.0010793861 -7.232830e-03
   [6,] -0.0061656492  0.0083643541 -2.758769e-03
   [7,] -0.0080114199 -0.0062033197  4.553525e-03
   [8,]  0.0006888202  0.0076752447  9.725048e-03
   [9,] -0.0029419869  0.0079036858 -7.495971e-03
  [10,] -0.0011790375  0.0097713338 -1.202918e-03
  [11,]  0.0012250383 -0.0015515248  2.447500e-03
  [12,]  0.0018398017 -0.0070728049 -3.026401e-03
  [13,]  0.0087450867 -0.0062946529 -1.394902e-03
  [14,]  0.0065550766 -0.0069294610  2.396136e-03
  [15,] -0.0030396846  0.0028311002  8.503313e-03
  [16,] -0.0019680972  0.0026498845 -3.977120e-03
  [17,]  0.0035768726  0.0049580126  6.830826e-03
  [18,]  0.0006256678  0.0088542781  2.780382e-03
  [19,] -0.0099711034  0.0021352943  8.744064e-04
  [20,] -0.0085101676 -0.0087703394 -7.252224e-03
  [21,] -0.0028271391 -0.0032887361 -1.828240e-03
  [22,] -0.0079838673  0.0043019465  7.502448e-03
  [23,]  0.0001737718 -0.0017162139 -4.108727e-03
  [24,]  0.0093835945  0.0050491594  1.934096e-03
  [25,] -0.0020036394  0.0068283860 -1.484930e-03
  [26,] -0.0082353506  0.0080715668 -7.904536e-03
  [27,]  0.0005767927  0.0041543067  2.803204e-03
  [28,]  0.0086768129 -0.0080712857  2.699075e-05
  [29,]  0.0038968253 -0.0061933544  7.918596e-03
  [30,] -0.0031470991 -0.0058318664 -8.436244e-03
  [31,] -0.0029613733 -0.0047185251 -3.841216e-03
  [32,]  0.0069886823 -0.0077702175  6.599277e-03
  [33,]  0.0024468334 -0.0078867235 -8.836028e-03
  [34,]  0.0080644684  0.0049693606  7.185206e-03
  [35,] -0.0026684931  0.0094337330 -4.310124e-03
  [36,]  0.0097486683  0.0071273835 -4.622817e-03
  [37,] -0.0031669651  0.0079496680 -5.809397e-03
  [38,]  0.0057971367 -0.0064638016  6.931980e-03
  [39,] -0.0067298187 -0.0079483790 -4.908143e-03
  [40,]  0.0069486917  0.0028332293  8.764649e-03
  [41,] -0.0081894291  0.0058049546  3.807404e-03
  [42,]  0.0051319804  0.0005169622 -3.146600e-03
  [43,]  0.0068124540 -0.0014106342  2.722901e-03
  [44,] -0.0003855060 -0.0007895493 -4.236341e-03
  [45,]  0.0012020314  0.0072803056 -5.930592e-03
  [46,]  0.0010315306 -0.0062901867 -7.717957e-03
  [47,]  0.0081570238  0.0025854880 -2.287048e-03
  [48,] -0.0017180972 -0.0031161538  7.893594e-03
  [49,] -0.0027189799  0.0051972852 -7.580571e-03
  [50,] -0.0016389407 -0.0077337271  1.732173e-03

Code
  gen_bkg_noise(n = 50, num_dims = 3, mean = 5, sd = 2)
Output
             [,1]      [,2]      [,3]
   [1,]  7.635531 10.037518  1.292495
   [2,]  3.715683  5.483040  7.456922
   [3,]  2.415887  7.175448  5.392385
   [4,]  7.569624  3.788400  4.513048
   [5,]  5.755002  7.298743 10.601484
   [6,]  4.691712  4.376551  2.787309
   [7,]  1.935748  5.351451  6.788723
   [8,]  5.781366  2.118594  6.608606
   [9,]  4.071065  2.998375  4.881167
  [10,] 10.958479  4.780378  5.959907
  [11,]  5.724177  5.462357  2.943693
  [12,]  4.956438  6.080202  6.750636
  [13,]  5.507636  5.374392  2.562168
  [14,]  4.855293  4.282226  5.279027
  [15,]  3.980759  2.482610  3.342656
  [16,]  5.760223  6.003400  5.325136
  [17,]  4.376727  8.140154  6.354585
  [18,]  5.681875  6.139086  7.107791
  [19,]  5.815919  6.616105  7.024455
  [20,]  6.960276  6.320212  8.371258
  [21,]  7.675015  4.006200  4.887007
  [22,]  3.005959  4.303653  2.737309
  [23,]  4.697547  6.658578  4.123089
  [24,]  2.344723  5.581362  8.613646
  [25,]  5.695303  7.340288  5.139200
  [26,]  5.744492  2.461405  4.926796
  [27,]  5.220763  5.547385  5.350338
  [28,]  5.271393  5.160677  7.293827
  [29,]  3.242984  7.635102  2.667537
  [30,]  7.509159  5.929393  4.273409
  [31,]  4.608606  6.609514  1.566371
  [32,]  3.209056  2.180772  6.178835
  [33,]  5.725237  5.165021  4.623872
  [34,]  6.335796  5.656328  7.121541
  [35,]  5.541793  5.284546  3.879901
  [36,]  4.150795  3.361770  9.522660
  [37,]  4.566437  3.853464  8.282124
  [38,]  7.000616  4.294237  4.797790
  [39,]  6.093445  8.901827  6.905468
  [40,]  3.246655  8.315616  7.166919
  [41,]  3.738327  3.308575  2.781573
  [42,]  2.500154  3.215103  2.874195
  [43,]  8.812362  7.008436  3.795285
  [44,]  7.534683  4.155437  5.174475
  [45,]  2.466038  3.223680  3.174031
  [46,]  6.614562  3.962873 -0.761985
  [47,]  4.644544  4.439052  4.704260
  [48,]  7.197077  2.669747  3.499666
  [49,]  5.659864  3.934118  4.061197
  [50,]  6.411732  7.543840  8.805822